Why is it useful to track loss while model is being trained?
The loss function (aka cost function) measures the correctness of the predictions of the model. 

For example, a simple cost function could be $|y - f(x)|$, where 


$\hat{y} = f(x)$ is the prediction of the model $f$ when the input is $x$,
$y$ is the ground-truth label for input $x$ (i.e. what the model is supposed to output when the input is $x$), and
$|\cdot|$ is the absolute value


The loss function is also used to understand whether a model is overfitting (and underfitting) or not. More specifically, if the loss of the model evaluated on the training data (the training loss) keeps decreasing while the loss of the model evaluated on the validation data (validation loss) starts to increase, that's a good sign that the model is overfitting (i.e. just memorizing the training data, but it will likely perform poorly on non-training data). If the training loss does not decrease, that's a good sign of underfitting (i.e. the model is not capable of learning the patterns in the training data), so you may need to use a model with a bigger capacity, change the loss, or do something else.

The loss is typically not directly used as a measure of the performance of the model because the loss doesn't directly represent the performance of the model. In fact, the loss can be relatively big (with respect to another model's loss) and the model still performs well, but typically a lower loss corresponds to higher performance. The performance of the model is calculated differently depending on the task, model, and your goals. The most common performance measure is the accuracy (the number of correct predictions over the total number of predictions), but there are many other performance measures, such as the precision, recall, f1 score, AUC, etc., that emphasize different behavior that you expect from your model.